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Discrete optimization problems are often of practical real-world importance as well as computationally intractable. For example, the traveling salesperson, bin packing, and longest common subsequence problems are NP-Hard, as is resource constrained scheduling, and many single-machine scheduling problems (Garey & Johnson, 1979). Polynomial time algorithms for such problems are unlikely to exist, and the best known algorithms that guarantee optimal solutions have a worst-case exponential runtime. It is thus common to use stochastic local search and other metaheuristics (Gonzalez, 2018). Stochastic local search algorithms begin at a random search state, and apply a sequence of neighbor transitions to nearby search states. This includes perturbative (Hoos & Stützle, 2018) algorithms like simulated annealing (Delahaye, Chaimatanan, & Mongeau, 2019) and hill climbers (Hoos & Stützle, 2018), where each search state is a complete candidate feasible solution, and a mutation operator makes a small random modification to move to another local candidate solution; and also includes constructive (Hoos & Stützle, 2018) algorithms like stochastic samplers (Bresina, 1996; Cicirello & Smith, 2005; Grasas, Juan, Faulin, de Armas, & Ramalhinho, 2017; Langley, 1992; ReyesRubiano, Calvet, Juan, Faulin, & Bové, 2020), where each search state is a partial solution that is iteratively transformed into a complete solution. Stochastic local search algorithms do not guarantee optimal solutions. However, they often find near-optimal solutions in much less time than systematic search. They also offer an anytime property (Jesus, Liefooghe, Derbel, & Paquete, 2020; Zilberstein, 1996), where solution quality improves with runtime.

Chips-n-Salsa: A Java Library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms